Applied Regression Analysis and Other Multivariable Methods 5th Edition by David Kleinbaum, Lawrence Kupper, Azhar Nizam, Eli Rosenberg 1285971671 9781285971674

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ISBN 10: 1285971671

ISBN 13: 9781285971674

Author: David G. Kleinbaum, Lawrence L. Kupper, Azhar Nizam, Eli S. Rosenberg

This bestseller will help you learn regression-analysis methods that you can apply to real-life problems. It highlights the role of the computer in contemporary statistics with numerous printouts and exercises that you can solve using the computer. The authors continue to emphasize model development, the intuitive logic and assumptions that underlie the techniques covered, the purposes, advantages, and disadvantages of the techniques, and valid interpretations of those techniques.

Applied Regression Analysis and Other Multivariable Methods 5th Table of contents:

Chapter 1: Concepts and Examples of Research
1.1 Concepts
1.2 Examples
1.3 Concluding Remarks
References

Chapter 2: Classification of Variables and the Choice of Analysis
2.1 Classification of Variables
2.2 Overlapping of Classification Schemes
2.3 Choice of Analysis
References

Chapter 3: Basic Statistics: A Review
3.1 Preview
3.2 Descriptive Statistics
3.3 Random Variables and Distributions
3.4 Sampling Distributions of t, x², and F
3.5 Statistical Inference: Estimation
3.6 Statistical Inference: Hypothesis Testing
3.7 Error Rates, Power, and Sample Size
Problems
References

Chapter 4: Introduction to Regression Analysis
4.1 Preview
4.2 Association versus Causality
4.3 Statistical versus Deterministic Models
4.4 Concluding Remarks
References

Chapter 5: Straight-line Regression Analysis
5.1 Preview
5.2 Regression with a Single Independent Variable
5.3 Mathematical Properties of a Straight Line
5.4 Statistical Assumptions for a Straight-line Model
5.5 Determining the Best-fitting Straight Line
5.6 Measure of the Quality of the Straight-line Fit and Estimate of σ²
5.7 Inferences about the Slope and Intercept
5.8 Interpretations of Tests for Slope and Intercept
5.9 The Mean Value of Y at a Specified Value of X
5.10 Prediction of a New Value of Y at X₀
5.11 Assessing the Appropriateness of the Straight-line Model
5.12 Example: BRFSS Analysis
Problems
References

Chapter 6: The Correlation Coefficient and Straight-line Regression Analysis
6.1 Definition of r
6.2 r as a Measure of Association
6.3 The Bivariate Normal Distribution
6.4 r² and the Strength of the Straight-line Relationship
6.5 What r² Does Not Measure
6.6 Tests of Hypotheses and Confidence Intervals for the Correlation Coefficient
6.7 Testing for the Equality of Two Correlations
6.8 Example: BRFSS Analysis
6.9 How Large Should r² Be in Practice?
Problems
References

Chapter 7: The Analysis-of-Variance Table
7.1 Preview
7.2 The ANOVA Table for Straight-line Regression
Problems

Chapter 8: Multiple Regression Analysis: General Considerations
8.1 Preview
8.2 Multiple Regression Models
8.3 Graphical Look at the Problem
8.4 Assumptions of Multiple Regression
8.5 Determining the Best Estimate of the Multiple Regression Equation
8.6 The ANOVA Table for Multiple Regression
8.7 Example: BRFSS Analysis
8.8 Numerical Examples
Problems
References

Chapter 9: Statistical Inference in Multiple Regression
9.1 Preview
9.2 Test for Significant Overall Regression
9.3 Partial F Test
9.4 Multiple Partial F Test
9.5 Strategies for Using Partial F Tests
9.6 Additional Inference Methods for Multiple Regression
9.7 Example: BRFSS Analysis
Problems
References

Chapter 10: Correlations: Multiple, Partial, and Multiple Partial
10.1 Preview
10.2 Correlation Matrix
10.3 Multiple Correlation Coefficient
10.4 Relationship of Rᵧ|X₁, X₂, …, Xₖ to the Multivariate Normal Distribution
10.5 Partial Correlation Coefficient
10.6 Alternative Representation of the Regression Model
10.7 Multiple Partial Correlation
10.8 Concluding Remarks
Problems
References

Chapter 11: Confounding and Interaction in Regression
11.1 Preview
11.2 Overview
11.3 Interaction in Regression
11.4 Confounding in Regression
11.5 Summary and Conclusions
Problems
References

Chapter 12: Dummy Variables in Regression
12.1 Preview
12.2 Definitions
12.3 Rule for Defining Dummy Variables
12.4 Comparing Two Straight-line Regression Equations: An Example
12.5 Questions for Comparing Two Straight Lines
12.6 Methods of Comparing Two Straight Lines
12.7 Method I: Using Separate Regression Fits to Compare Two Straight Lines
12.8 Method II: Using a Single Regression Equation to Compare Two Straight Lines
12.9 Comparison of Methods I and II
12.10 Testing Strategies and Interpretation: Comparing Two Straight Lines
12.11 Other Dummy Variable Models
12.12 Comparing Four Regression Equations
12.13 Comparing Several Regression Equations Involving Two Nominal Variables
Problems
References

Chapter 13: Analysis of Covariance and Other Methods for Adjusting Continuous Data
13.1 Preview
13.2 Adjustment Problem
13.3 Analysis of Covariance
13.4 Assumption of Parallelism: A Potential Drawback
13.5 Analysis of Covariance: Several Groups and Several Covariates
13.6 Analysis of Covariance: Several Nominal Independent Variables
13.7 Comments and Cautions
13.8 Summary
Problems
References

Chapter 14: Regression Diagnostics
14.1 Preview
14.2 Simple Approaches to Diagnosing Problems in Data
14.3 Residual Analysis: Detecting Outliers and Violations of Model Assumptions
14.4 Strategies for Addressing Violations of Regression Assumptions
14.5 Collinearity
14.6 Diagnostics Example
Problems
References

Chapter 15: Polynomial Regression
15.1 Preview
15.2 Polynomial Models
15.3 Least-squares Procedure for Fitting a Parabola
15.4 ANOVA Table for Second-order Polynomial Regression
15.5 Inferences Associated with Second-order Polynomial Regression
15.6 Example Requiring a Second-order Model
15.7 Fitting and Testing Higher-order Models
15.8 Lack-of-fit Tests
15.9 Orthogonal Polynomials
15.10 Strategies for Choosing a Polynomial Model
Problems

Chapter 16: Selecting the Best Regression Equation
16.1 Preview
16.2 Steps in Selecting the Best Regression Equation: Prediction Goal
16.3 Step 1: Specifying the Maximum Model: Prediction Goal
16.4 Step 2: Specifying a Criterion for Selecting a Model: Prediction Goal
16.5 Step 3: Specifying a Strategy for Selecting Variables: Prediction Goal
16.6 Step 4: Conducting the Analysis: Prediction Goal
16.7 Step 5: Evaluating Reliability with Split Samples: Prediction Goal
16.8 Example Analysis of Actual Data
16.9 Selecting the Most Valid Model
Problems
References

Chapter 17: One-way Analysis of Variance
17.1 Preview
17.2 One-way ANOVA: The Problem, Assumptions, and Data Configuration
17.3 Methodology for One-way Fixed-effects ANOVA
17.4 Regression Model for Fixed-effects One-way ANOVA
17.5 Fixed-effects Model for One-way ANOVA
17.6 Random-effects Model for One-way ANOVA
17.7 Multiple-comparison Procedures for Fixed-effects One-way ANOVA
17.8 Choosing a Multiple-comparison Technique
17.9 Orthogonal Contrasts and Partitioning an ANOVA Sum of Squares
Problems
References

Chapter 18: Randomized Blocks: Special Case of Two-way ANOVA
18.1 Preview
18.2 Equivalent Analysis of a Matched-pairs Experiment
18.3 Principle of Blocking
18.4 Analysis of a Randomized-blocks Study
18.5 ANOVA Table for a Randomized-blocks Study
18.6 Regression Models for a Randomized-blocks Study
18.7 Fixed-effects ANOVA Model for a Randomized-blocks Study
Problems
References

Chapter 19: Two-way ANOVA with Equal Cell Numbers
19.1 Preview
19.2 Using a Table of Cell Means
19.3 General Methodology
19.4 F Tests for Two-way ANOVA
19.5 Regression Model for Fixed-effects Two-way ANOVA
19.6 Interactions in Two-way ANOVA
19.7 Random- and Mixed-effects Two-way ANOVA Models
Problems
References

Chapter 20: Two-way ANOVA with Unequal Cell Numbers
20.1 Preview
20.2 Presentation of Data for Two-way ANOVA: Unequal Cell Numbers
20.3 Problem with Unequal Cell Numbers: Nonorthogonality
20.4 Regression Approach for Unequal Cell Sample Sizes
20.5 Higher-way ANOVA
Problems
References

Chapter 21: The Method of Maximum Likelihood
21.1 Preview
21.2 The Principle of Maximum Likelihood
21.3 Statistical Inference Using Maximum Likelihood
21.4 Summary
Problems
References

Chapter 22: Logistic Regression Analysis
22.1 Preview
22.2 The Logistic Model
22.3 Estimating the Odds Ratio Using Logistic Regression
22.4 A Numerical Example of Logistic Regression
22.5 Theoretical Considerations
22.6 An Example of Conditional ML Estimation Involving Pair-matched Data with Unmatched Covariates
22.7 Summary
Problems
References

Chapter 23: Polytomous and Ordinal Logistic Regression
23.1 Preview
23.2 Why Not Use Binary Regression?
23.3 An Example of Polytomous Logistic Regression: One Predictor, Three Outcome Categories
23.4 An Example: Extending the Polytomous Logistic Model to Several Predictors
23.5 Ordinal Logistic Regression: Overview
23.6 A “Simple” Example: Three Ordinal Categories and One Dichotomous Exposure Variable
23.7 Ordinal Logistic Regression Example Using Real Data with Four Ordinal Categories
23.8 Summary
Problems
References

Chapter 24: Poisson Regression Analysis
24.1 Preview
24.2 The Poisson Distribution
24.3 An Example of Poisson Regression
24.4 Poisson Regression
24.5 Measures of Goodness of Fit
24.6 Continuation of Skin Cancer Data Example
24.7 A Second Illustration of Poisson Regression Analysis
24.8 Summary
Problems
References

Chapter 25: Analysis of Correlated Data Part 1: The General Linear Mixed Model
25.1 Preview
25.2 Examples
25.3 General Linear Mixed Model Approach
25.4 Example: Study of Effects of an Air Pollution Episode on FEV1 Levels
25.5 Summary
Problems
References

Chapter 26: Analysis of Correlated Data Part 2: Random Effects and Other Issues
26.1 Preview
26.2 Random Effects Revisited
26.3 Results for Models with Random Effects Applied to Air Pollution Study Data
26.4 Second Example – Analysis of Posture Measurement Data
26.5 Recommendations about Choice of Correlation Structure
26.6 Analysis of Data for Discrete Outcomes
Problems
References

Chapter 27: Sample Size Planning for Linear and Logistic Regression and Analysis of Variance
27.1 Preview
27.2 Review: Sample Size Calculations for Comparisons of Means and Proportions
27.3 Sample Size Planning for Linear Regression
27.4 Sample Size Planning for Logistic Regression
27.5 Power and Sample Size Determination for Linear Models
27.6 Sample Size Determination for Matched Case-control Studies
27.7 Practical Considerations and Cautions
Problems
References

Appendices
Appendix A: Tables
Appendix B: Matrices and Their Relationship to Regression Analysis
Appendix C: SAS Computer Appendix
Appendix D: Answers to Selected Problems

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